Computational Finance
This page contains resources about Computational Finance, Financial Engineering, Mathematical Finance and Quantitative Finance. Subfields and Concepts * Binomial Options Pricing Model * Black–Scholes Model * Capital Asset Pricing Model (CAPM) * Markowitz Model / Mean-Variance Model * Markov property * Martingale property * Efficient Market Hypothesis (EMH) * Capital Market Line * Financial Signal Processing * Financial Portfolio Management / Asset Allocation * Financial Risk Management ** Value at Risk (VaR) ** Sharpe ratio ** Dispersion ** Drawdown ** Maximum Drawdown ** Alpha ** Beta Online courses Video Lectures * Computational Finance by Steven Skiena * Financial Engineering and Risk Management Part I by Martin Haugh and Garud Iyengar * Financial Engineering and Risk Management Part II by Martin Haugh and Garud Iyengar * Machine Learning and Reinforcement Learning in Finance Specialization by Igor Halperin Lectures Notes * Computational Finance by Christian Bayer and Antonis Papantoleon * Introduction to computational finance and financial econometrics with R by Rric Zivot * Class Notes in Statistics and Econometrics by Hans G. Ehrbar * Computational Methods in Finance by Jonathan Goodman * Financial Mathematics by Harald Lang * Financial Mathematics I by Jitse Niesen * Introduction to Financial Mathematics by P. V. Johnson * Topics in Mathematics with Applications in Finance by Peter Kempthorne, Choongbum Lee, Vasily Strela and Jake Xia Books and Book Chapters S''ee also Reading List.'' *Lachowicz, P. (TBA). Python for Quants. Volume II. QuantAtRisk eBooks. *de Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons. *Yan, Y. (2017). Python for Finance. 2nd Ed. Packt Publishing Ltd. *Akansu, A. N., Kulkarni, S. R., & Malioutov, D. M. (Eds.). (2016). Financial Signal Processing and Machine Learning. John Wiley & Sons. *Akansu, A. N., & Torun, M. U. (2015). A primer for financial engineering: financial signal processing and electronic trading. Academic Press. *Lachowicz, P. (2015). Python for Quants. Volume I. QuantAtRisk eBooks. *Hilpisch, Y. (2015). Derivatives Analytics with Python: Data Analysis, Models, Simulation, Calibration and Hedging. John Wiley & Sons. *Skoglund, J., & Chen, W. (2015). Financial risk management: Applications in market, credit, asset and liability management and firmwide risk. John Wiley & Sons. *Hull, J. (2015). Risk management and financial institutions. 4th Ed. John Wiley & Sons. *John, C. (2014). Options, Futures and other Derivative Securities. 9th Ed. Prentice HaII. *Hilpisch, Y. (2014). Python for Finance: Analyze Big Financial Data. O'Reilly Media. *Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2014). Modern portfolio theory and investment analysis. 9th Ed. John Wiley & Sons. *Benninga, S. (2014). Financial modeling. MIT Press. *Crack, T. F. (2014). Heard on the Street: Quantitative Questions from Wall Street Job Interviews. 15th Ed. Timothy Crack. *Crouhy, M., Galai, D., & Mark, R. (2014). The essentials of risk management. 2nd Ed. McGraw-Hill. *Chatterjee, R. (2014). Practical methods of financial engineering and risk management: tools for modern financial professionals. Apress. *Blyth, S. (2013). An introduction to quantitative finance. Oxford University Press. *Joshi, M. S., & Paterson, J. M. (2013). Introduction to Mathematical Portfolio Theory. Cambridge University Press. *Joshi, M. S., Denson, N., & Downes, A. (2013). Quant Job Interview: Questions and Answers. 2nd Ed. Pilot Whale Press. * McKinney, W. (2012). Python for data analysis: Data wrangling with Pandas, NumPy, and IPython. O'Reilly Media. *Steland, A. (2012). Financial statistics and mathematical finance: methods, models and applications. John Wiley & Sons. *Hirsa, A. (2012). Computational methods in finance. CRC Press. *Alhabeeb, M. J. (2012). Mathematical finance. John Wiley & Sons. *Boyarshinov, V. (2012). Machine learning in computational finance: Practical algorithms for building artificial intelligence applications. LAP LAMBERT Academic Publishing. *Allen, S. (2012). Financial Risk Management: A Practitioner's Guide to Managing Market and Credit Risk. 2nd Ed. John Wiley & Sons. *Joshi, M. S. (2011). More Mathematical Finance. Pilot Whale. *Stefanica, D. (2011). A primer for the Mathematics of Financial Engineering. Fe Press. *Duffie, D. (2010). Dynamic asset pricing theory. Princeton University Press. *Tsay, R. S. (2010). Analysis of Financial Time Series. 3rd Ed. John Wiley & Sons. *Kennedy, D. (2010). Stochastic financial models. Chapman and Hall/CRC. *Jeanblanc, M., Yor, M., & Chesney, M. (2009). Mathematical methods for financial markets. Springer Science & Business Media. *Meucci, A. (2009). Risk and asset allocation. Springer Science & Business Media. *Zhou, X. (2008). A Practical Guide to Quantitative Finance Interviews. 14th Ed. CreateSpace. *Joshi, M. S. (2008). The concepts and practice of mathematical finance. 2nd Ed. Cambridge University Press. *Brooks, C. (2008). Introductory econometrics for finance. 2nd Ed. Cambridge University Press. *Bacon, C. R. (2008). Practical Portfolio Performance Measurement and Attribution. 2nd Ed. John Wiley & Sons. *Fusai, G., & Roncoroni, A. (2008). Implementing models in quantitative finance: methods and cases. Springer Science & Business Media. *Wilmott, P. (2007). Paul Wilmott introduces quantitative finance. John Wiley & Sons. *Estrada, J. (2007). Finance in a Nutshell: A No-nonsense Companion to the Tools and Techniques of Finance. Pearson Education. *Seydel, R., & Seydel, R. (2006). Tools for computational finance. Springer. *Brandimarte, P. (2006). Numerical methods in finance and economics: a MATLAB-based introduction. 2nd Ed. John Wiley & Sons. *Higham, D. (2004). An introduction to financial option valuation: mathematics, stochastics and computation. Cambridge University Press. *Joshi, M. S. (2004). More Mathematical Finance. Cambridge University Press. *Joshi, M. S. (2004). The concepts and practice of mathematical finance. Cambridge University Press. *Cuthbertson, K., & Nitzsche, D. (2004). Quantitative financial economics: stocks, bonds and foreign exchange. 2nd Ed. John Wiley & Sons. *Glasserman, P. (2003). Monte Carlo methods in financial engineering. Springer Science & Business Media. *Feibel, B. J. (2003). Investment performance measurement. John Wiley & Sons. *Jackel, P. (2002). Monte Carlo methods in finance. John Wiley & Sons. *Cuthbertson, K., & Nitzsche, D. (2001). Financial engineering: derivatives and risk management. John Wiley & Sons. *Karatzas, I., & Shreve, S. E. (1998). Methods of mathematical finance. Springer Science & Business Media. *Luenberger, D. G. (1997). Investment science. Oxford University Press. *Campbell, J. Y., Lo, A. W. C., & MacKinlay, A. C. (1997). The econometrics of financial markets. ''2nd Ed. Princeton University Press. *Baxter, M., & Rennie, A. (1996). ''Financial calculus: an introduction to derivative pricing. Cambridge University Press. *Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton University Press. Scholarly Articles *Boyd, S., Busseti, E., Diamond, S., Kahn, R. N., Koh, K., Nystrup, P., & Speth, J. (2017). Multi-period trading via convex optimization. Foundations and Trends® in Optimization, 3(1), 1-76. * Feng, Y., & Palomar, D. P. (2016). A signal processing perspective on financial engineering. Foundations and Trends® in Signal Processing, 9(1–2), 1-231. * Bonanno, G., Caldarelli, G., Lillo, F., Micciche, S., Vandewalle, N., & Mantegna, R. N. (2004). Networks of equities in financial markets. The European Physical Journal B, 38(2), 363-371. Software * Financial Toolbox - MATLAB * Computational Finance - MATLAB * dawp - Python * Sage * QuantLib - C#, Objective Caml, Java, Perl, Python, GNU R, Ruby, and Scheme * DX Analytics - Python * QuantEcon.py * zipline - Python * finmarketpy - Python * Lean - Python, C#, F# * backtrader - Python See also * Probability and Statistics * Stochastic Processes * State Space Models / Time Series * Nonlinear Dynamical Systems / Chaos Theory * Monte Carlo Methods * Statistical Signal Processing / Estimation Theory * Digital Signal Processing * Optimization / Operations Research * Artificial Neural Networks Other Resources * QuantStart * Quantopian * What are the best blogs about quantitative trading? - Quora * Algorithmic trading in less than 100 lines of Python code * Awesome-Quant - Github * Marco Avellaneda - List of lecture notes * Neural networks for algorithmic trading: enhancing classic strategies - Blog post * Neural networks for algorithmic trading. Multivariate time series - Blog post * Neural networks for algorithmic trading. Simple time series forecasting - Blog post * Deep Learning the Stock Market - Blog post, with code * Yahoo! Finance - datasets * Quandl - datasets * Alpha Vantage - datasets * Quantopian Data - datasets * Quantiacs - Markets - datasets * PyFin (Medium) - blog * Python-for-Data-Science (GitHub) - code * Python for Finance - blog * Python for Finance: Stock Portfolio Analyses (Medium) - blog post * Python For Finance: Algorithmic Trading (DataCamp) - blog post * py4fi (GitHub) - code * Python for Finance (Part 1 , Part 2, Part 3) - blog posts with code * QuantInsti’s Blog on Algo Trading and Quantitative Finance * quant-finance (GitHub) - code * Markowitz Portfolio Optimization with Python - blog post * Efficient Frontier Portfolio Optimisation in Python - blog post * Investment Portfolio Optimization - blog post * The Efficient Frontier: Markowitz portfolio optimization in Python - blog post * QuantAndFinancial - blog with code * portfolioopt (GitHub) - code * New-York-Stock-Exchange-Predictions-RNN-LSTM (GitHub) - code * Datasets on Finance (Kaggle) * Predict Stock Prices Using RNN (Part 1, Part 2) - blog post * Stock Market Predictions with LSTM in Python - blog post * Stock prediction LSTM using Keras (Kaggle) * Predict stock prices with LSTM (Kaggle) Category:Computational Finance